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https://csucidatamining.weebly.com/assign-7.html
(a) A rule that has high support and high confidence. Support is how often a rule is applicable for the provided data set. Confidence is how frequently an item in Y appear in transactions that contain X. Such rules are not subjectively interesting due to any household that has children would use milk and diapers.
https://www.researchgate.net/publication/233754781_Support_vs_Confidence_in_Association_Rule_Algorithms
Support vs Confidence in Association Rule Algorithms. The discovery of interesting association relationships among large amounts of business transactions is currently vital for making appropriate business decisions. There are currently a variety of algorithms to discover association rules.
https://pdfs.semanticscholar.org/5a2e/b48391606e735f4ff20a464de12dee062168.pdf
to derive high confidence rules regardless of their support level [11]. In the current work an attempt has been made to generate relevant rules irrespective of the support but without using any specialized data structures or data warehousing-related operations. In this work a search term/seed itemset is used to generate only those itemsets
https://stats.stackexchange.com/questions/229523/association-rules-support-confidence-and-lift
However, I just don't know how to interpret rules with these indicators. I have rules with high support, high confidence and low lift, is that a good rule ? Since high confidence represents strong association and high support represents how convincing their association are. So high confidence + high support = good rule and we can ignore lift? If I am going to order / rank my rules and pick, let …
https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/algo_apriori.htm
Both support and confidence must be used to determine if a rule is valid. However, there are times when both of these measures may be high, and yet still produce a rule that is not useful. For example: Convenience store customers who buy orange juice also buy milk with a 75% confidence. The combination of milk and orange juice has a support of 30%.
https://www.coursehero.com/tutors-problems/Computer-Science/9165275-Need-help-with-these-question-Answer-question-with-minimum-of-200-word/
Also, describe whether such rules are subjectively interesting. A pattern is subjectively interesting if it contradicts the expectation of a user and if it is actionable. 1. A rule that has high support and high confidence 2. A rule that has reasonably high support but low confidence 3. A rule that has low support and low confidence 4.5/5(1)
https://www.solver.com/xlminer/help/association-rules
The first number is called the support for the rule. The support is simply the number of transactions that include all items in the antecedent and consequent parts of the rule. The support is sometimes expressed as a percentage of the total number of records in the database.) The other number is known as the confidence of the rule. Confidence is the ratio of the number of transactions that include all items in the consequent…
https://www.academia.edu/648890/Support_vs_Confidence_in_Association_Rule_Algorithms
However its difference is that instead of having to Support vs Confidence in Association Rule Algorithms 7 count the support for each itemset by scanning the database multiple times, enough data can be collected during the 1-itemset generation scan to deduce the support for any consequent k-itemset (Yen et al., 1997).
https://www.slideshare.net/zafarjcp/data-mining-association-rules-basics
Dec 06, 2009 · Mining Association Rules: 11 Two Step Approach Frequent Itemset Generation Generate all itemsets whose support minsup Rule Generation Generate high confidence rules from frequent itemset Each rule is a binary partitioning of a frequent itemset Frequent itemset generation is computationally expensive Prof. Pier Luca Lanzi
https://en.wikipedia.org/wiki/Association_rule_learning
High-order pattern discovery facilitate the capture of high-order (polythetic) patterns or event associations that are intrinsic to complex real-world data. K-optimal pattern discovery provides an alternative to the standard approach to association rule learning that requires that each pattern appear frequently in the data.
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